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Statistical Signal Processing
This page contains resources about Statistical Signal Processing, Point Estimation, Estimation Theory, Adaptive Filtering, Adaptive Signal Processing and Adaptive Filter Theory, and System Identification. Subfields and Concepts * Least mean squares (LMS) filter * Kernel least mean squares (KLMS) filter * Online Estimation / Recursive Estimation ** Recursive least squares (RLS) filter / Forgetting Factor ** Kernel recursive least (KRLS) squares ** Kalman filter ** Extended Kalman filter (EKF) ** Unscented Kalman filter (UKF) * Normalised least mean squares (NLMS) filter * Hierarchical least mean squares (HLMS) filter * Complex valued filters ** Complext least mean squares (CLMS) filter * Square root filter * Monte Carlo Methods ** Particle filter * Bayesian Linear Regression * Regularization ** Regularized least squares * Iterative Methods ** Levenberg–Marquardt Algorithm ** Iteratively Reweighted Least Squares ** Nonlinear Least Squares ** Gradient Descent / Steepest Descent ** Broyden–Fletcher–Goldfarb–Shanno (BFGS) Algorithm * Blind adaptive filter ** Blind Deconvolution * Artificial Neural Networks ** Back-Propagation Algorithm * Bayesian Point Estimation / Bayesian Parameter Estimation / Bayesian Methods ** Wiener filter ** Bayes filter / Recursive Bayesian Estimation *** Kalman filter ** Nonparametric Empirical Bayes (NPEB) ** Parametric Empirical Bayes Point Estimation ** Maximum a posteriori (MAP) ** Maximum likelihood estimation (MLE) * Methods for finding estimators ** Method of Moments ** Maximum Likelihood Estimator ** Bayes Estimator / Bayesian Decision Theory *** Conjugate prior *** Exponential family ** Expectation-Maximization Algorithm ** Minimum-variance mean-unbiased estimator (MVUE) ** Median-unbiased estimator * Methods for evaluating estimators ** Minimum mean squared error (MMSE) / Bayes least squared error (BLSE) ** Best linear unbiased estimator (BLUE) ** James–Stein estimator * M-estimators ** MLE ** Steepest Descent / Gradient Descent *** Stochastic Gradient Descent (SGD) *** Generalised Normalised Gradient Descent (GNGD) *** Hierarchical Gradient Descent (HGD) * Cramér–Rao bound * Black-box and grey-box models * Parametric and nonparametric models * Time Series Models ** ARMA Models ** Volatility Models / Conditional Heteroscedastic Models / ARCH Models ** ARMA Metrics (e.g. Martin Distance) * State Space Models ** Kalman filter ** State Space Models with Regime Switching / Jump Markov Linear Systems ** Subspace Identification *** N4ASID *** MOESP *** CVA / CCA *** SSARX *** Frequency domain subspace identification *** Time domain subspace identification * Time domain methods * Frequency domain methods / Spectral Estimation ** Empirical transfer function estimation (ETFE) ** Periodogram * Stochastic Systems ** Stochastic Processes ** Stochastic Calculus ** Stochastic Optimal Control ** Stochastic Time Series * Minimum message length (Occam's Razor) * Nonlinear System Identification ** Artificial Neural Networks ** NARMAX Models ** Volterra Series Models ** Block Structured Models * Subspace Learning / Dimensionality Reduction ** Subspace Tracking *** Grassmannian Rank-One Update Subspace Estimation (GROUSE) *** Parallel Estimation and Tracking by REcursive Least Squares (PETRELS) *** Multiscale Online Union of Subspaces Estimation (MOUSSE) *** Grassmannian Robust Adaptive Subspace Tracking Algorithm (GRASTA) *** Online Supervised Dimensionality Reduction (OSDR) * Adaptive Control ** Auto-tuning / Self-tuning Online Courses Video Lectures * Adaptive filters by Ali H. Sayed * Learning Theory by Reza Shadmehr Lecture Notes * Advanced Signal Processing by Danilo Mandic * Spectral Estimation and Adaptive Filtering by Danilo Mandic * Stochastic Systems by Florian Herzog * System Identification by Roy Smith * Recursive Estimation by Rafaello D'Andrea Books and Book Chapters * Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons. * Kumar, P. R., & Varaiya, P. (2015). Stochastic systems: Estimation, identification, and adaptive control. SIAM. * Haykin , S. (2014). Adaptive filter theory. 5th Ed. Prentice Hall. * Tangirala, A. K. (2014). Principles of System Identification: Theory and Practice. CRC Press. * Goodwin, G. C., & Sin, K. S. (2014). Adaptive filtering prediction and control. Dover Publications. * Åström, K. J., & Wittenmark, B. (2013). Adaptive control. Dover Publications. * Van Trees, H. L. (2013). Detection Estimation and Modulation Theory, Part I: Detection, Estimation, and Filtering Theory. 2nd Ed. John Wiley & Sons. * Diniz, P. S. (2012). Adaptive filtering. Springer . * Sayed, A. H. (2011). Adaptive filters. John Wiley & Sons. * Adali, T., & Haykin, S. (2010). Adaptive signal processing: next generation solutions. John Wiley & Sons. * Hayes, M. H. (2009). Statistical digital signal processing and modeling. John Wiley & Sons. * Mandic, D. P., & Goh, V. S. L. (2009). Complex valued nonlinear adaptive filters: noncircularity, widely linear and neural models (Vol. 59). John Wiley & Sons. * Kulkarni, V. G. (2009). Modeling and analysis of stochastic systems. 2nd Ed. CRC Press. * Vaseghi, S. V. (2008). Advanced digital signal processing and noise reduction. John Wiley & Sons. * Van den Bos, A. (2007). Parameter estimation for scientists and engineers. John Wiley & Sons. * Poularikas, A. D., & Ramadan, Z. M. (2006). Adaptive filtering primer with MATLAB. CRC Press. * Manolakis, D. G., Ingle, V. K., & Kogon, S. M. (2005). Statistical and adaptive signal processing: spectral estimation, signal modeling, adaptive filtering, and array processing. Norwood: Artech House. * Gray, R. M., & Davisson, L. D. (2004). An introduction to statistical signal processing. Cambridge University Press. * Lehmann, E. L., & Casella, G. (2003). Theory of point estimation. Springer. * Casella, G., & Berger, R. L. (2002). Statistical inference. Cengage Learning. * Cichocki, A., & Amari, S. I. (2002). Adaptive blind signal and image processing: learning algorithms and applications. John Wiley & Sons. * Nelles, O. (2001). Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer Science & Business Media. * Lennart, L. (1999). System identification: theory for the user. PTR Prentice Hall, Upper Saddle River, NJ. * Van Overschee, P., & De Moor, B. L. (1996). Subspace identification for linear systems: Theory—Implementation—Applications. Springer. * Kay, S. M. (1993). Fundamentals of Statistical Signal Processing, Vol I: Estimation Theory. * Scharf, L. L. (1991). Statistical signal processing (Vol. 98). Reading, MA: Addison-Wesley. Scholarly Articles * De Cock, K., De Moor, B., & Leuven, K. U. (2003). Subspace identification methods''.'' Control systems robotics and automation, Vol. 1 of 3, pp. 933-979 Software * System Identification Toolbox - MATLAB See also * Optimization * Optimal Control * Robust Control Other Resources * Subspace Tracking by Laura Balzano Category:Signal Processing Category:Probability and Statistics Category:Control Theory Category:Machine Learning